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Many experiences unfold predictably over time. Memory for these temporal regularities enables anticipation of events multiple steps into the future. Because temporally predictable events repeat over days, weeks, and years, we must maintain-and potentially transform-memories of temporal structure to support adaptive behavior. We explored how individuals build durable models of temporal regularities to guide multistep anticipation. Healthy young adults (Experiment 1: N = 99, age range = 18-40 years; Experiment 2: N = 204, age range = 19-40 years) learned sequences of scene images that were predictable at the category level and contained incidental perceptual details. Individuals then anticipated upcoming scene categories multiple steps into the future, immediately and at a delay. Consolidation increased the efficiency of anticipation, particularly for events further in the future, but diminished access to perceptual features. Further, maintaining a link-based model of the sequence after consolidation improved anticipation accuracy. Consolidation may therefore promote efficient and durable models of temporal structure, thus facilitating anticipation of future events.

Countless policies are crafted with the intention of punishing all who do wrong or rewarding only those who do right. However, this requires accommodating certain mistakes: some who do not deserve to be punished might be, and some who deserve to be rewarded might not be. Six preregistered experiments (N = 3,484 U.S. adults) reveal that people are more willing to accept this trade-off in principle, before errors occur, than in practice, after errors occur. The result is an asymmetry such that for punishments, people believe it is more important to prevent false negatives (e.g., criminals escaping justice) than to fix them, and more important to fix false positives (e.g., wrongful convictions) than to prevent them. For rewards, people believe it is more important to prevent false positives (e.g., welfare fraud) than to fix them and more important to fix false negatives (e.g., improperly denied benefits) than to prevent them.

This article is about the two conflicting goals when teaching statistics or data science courses based on real-world data in a business school environment. We propose to look at structured socio-economic data about the Canadian economy. Canada was ranked 8th in 2017 by Open Data Watch (Government of Canada) for its data accessibility policy. Statistics Canada offers several ways to access data across its over 11,000 data tables. We built an R package to ease access to Statistics Canada's open economic data. With this package, we offer students another option to collect data about the Canadian economy.

The Oum Er-Rbia watershed faces a critical challenge in effectively disseminating the results of groundwater and surface water quality assessments to stakeholders and the public. This research aimed to address this challenge by developing a method for synthesizing and visualizing knowledge through web mapping. The methodology followed involved collecting data on surface and groundwater quality, hydrogeology, human impact, and topography. Subsequently, a needs analysis was conducted to identify the requirements of potential users (land managers, water users, researchers) to de- fine system functionalities. Spatial data was organized into thematic layers (e.g., hydrography, geology) with detailed attributes to address user queries and generate maps. Open-source technologies were employed to build a web-based system (SCIEM) allowing users to visualize, analyze, and explore water quality data. Finally, user evaluation confirmed SCIEM's effectiveness in disseminating and analyzing water quality information within the watershed. The developed web mapping system has successfully addressed the challenge of disseminating research results by establishing an online, interactive multi-scale mapping system for groundwater and surface water characterization. This system facilitates the presentation of maps with a simple click, making them accessible to both geospatial experts and non-specialists. As a result, stakeholders such as water managers, land-use planners, and even farmers and citizens can now access a comprehensive view of groundwater and surface water information. Additionally, the utilization of free and open-source software has proven to be a cost-effective and efficient approach. The interactive multi-scale mapping system (IMSMS) provides a valuable tool for disseminating water quality data within the Oum Er-Rbia watershed. Building on this success, future research can explore the potential of adapting the IMSMS framework to other regions facing water quality challenges. Additionally, incorporating new data types beyond water management could provide a more holistic understanding of the interconnected factors influencing water resources. [ABSTRACT FROM AUTHOR]

ABSTRACT There is a common agreement among archaeologists that assessing visibility in the field is essential to measure the accuracy of their observations. Archaeologists widely expect that low visibility negatively impacts the recovery rate of artefacts and sites during field‐walking surveys. However, they hold fundamentally divergent opinions on using recorded visibility values and on whether or how to weight the results. In this paper, I undertake a review and comparison of ground visibility assessments from three archaeological field‐walking surveys conducted in the eastern Mediterranean, all of which have published their data. Capitalizing on the availability of open data, I recode and analyse the algorithms employed in these surveys. The results highlight the impacts of weighting techniques, and I compare the maps produced with and without weighting. In all cases, the corrections do not substantially change the interpretations of the results at the scale of site identification. As such, this data‐driven experiment contributes to the ongoing debate on how to compare effectively and integrate data from various survey projects to study regional trends. [ABSTRACT FROM AUTHOR]

AbstractOpen science (OS) is widely recognized as a new method of knowledge sharing using digital technologies. Despite the potential benefits OS offers, there is a low level of inspiration toward adopting it among researchers in higher learning institutions. The purpose of this study was to assess the faculty adoption of OS and their practices toward data sharing at Health Sciences University in Tanzania. The study employed a cross-sectional design, whereby a total of 252 faculties were systematically sampled from two purposefully selected health universities. Using a survey, the study employed SPSS to generate descriptive statistics and a coding strategy using ATLAS.ti software to analyze qualitative resultant data. Moreover, regression analysis determined the predicting factors that hindered faculty adoption of OS and data-sharing practices. The findings demonstrate that faculty had poor data-sharing practices triggered by a lack of institutional data-sharing policies, fear of data misinterpretation, and parasitic use of clinical research datasets with other researchers. However, the presence of a data repository, journal guidelines, and intellectual property rights are instrumental variables that allow faculty to practice OS and data sharing. Therefore, university libraries should take affirmative action to create awareness of the importance of practicing OS and data sharing among faculty and researchers. Such undertakings will allow other researchers to carry out comprehensive follow-up research to meet the increasing demand for innovative health research output in Tanzania. [ABSTRACT FROM AUTHOR]

The GeoNet programme at GNS Science has monitored and managed data for volcanoes, earthquakes, landslides, and tsunami in Aotearoa New Zealand since 2001. Volcano monitoring data are collected from seismometers, acoustic sensors, GNSS receivers, webcams, remote gas monitoring sensors, and a range of environmental sensors, as well as manually during visits to volcanoes. The primary user of volcano data is the internal cross-specialised Volcano Monitoring Group (VMG), which fulfils the role of the national volcano observatory. GeoNet concentrates on automatic data collection and analysis, while supporting members of the VMG with manual data collection and interpretation. The application of open-data principles to both data and metadata has always been a core aspect of GeoNet; responses have been overwhelmingly positive, despite concerns regarding some high value, manually collected datasets. The website www.geonet.org.nz represents the primary data access portal. Data analysis and delivery applications are organised by data type rather than hazard, with no volcano-specific data applications. Most datasets have web-based and API delivery application options; both provide standard data formats from a cloud-based archive. One of the challenges for volcano data collection and management has been shifting from a reliance on manually collected data to automatic collection. Additionally, awareness of important questions related to Indigenous Māori data governance is increasing, although the associated impact is not yet understood. Overall, the current centralised, cooperative volcano monitoring and data collection and management system, which benefits from improved efficiency, interoperability, and data quality, has proved effective in Aotearoa New Zealand. Ongoing work aims to ensure optimal data collection and management for volcano monitoring and downstream activities. [ABSTRACT FROM AUTHOR]

The growing demand for alternative energy sources to alleviate environmental impacts highlights the need to move from fossil fuels to renewable energy. This study demonstrated the technical feasibility of using a solar photovoltaic (PV) system for the production of green hydrogen. This research examined electrical and power data from a PV plant in Irecê, Bahia, using open data sources to provide insights into the production of green hydrogen from renewable sources. The system mainly depends on the use of a renewable source, PV solar energy, integrated with batteries, electrolyzers, and hydrogen tanks. Electrolyzer, battery, and hydrogen tank sizing analysis for optimal hydrogen production was effectively conducted using HOMER Energy software. The predicted system topology prioritizes a local DC network, optimizing efficiency for electrolyzers that have inherently low efficiency. The electrolyzer simulation involves initial Python‐based sizing and comprehensive sizing with HOMER Energy software, ensuring accuracy within a 10% discrepancy limit. This highlights the importance of analytical calculations and optimization software for sizing more complex systems. [ABSTRACT FROM AUTHOR]

The Open Science movement is taking hold around the world, and language testers are taking part. In this Viewpoint, I discuss how sharing, collaborating, and building trust, guided by Open Science principles, benefit the language testing field. To help more language testers join in, I present a standard definition of Open Science and describe four ways language testing researchers can immediately partake. Overall, I share my views on how Open Science is an accelerating process that improves language testing as a scientific and humanistic field. [ABSTRACT FROM AUTHOR]

Open research data refer to publicly available scientific information that can be accessed free of charge, usually provided by public data sources. Users must comply with specific requirements set by the institutions providing the data and always acknowledge the source of the data when processing, transmitting, storing or publishing it. One of the tasks of the BrineRIS project is the mapping of brine resources, requiring reliable data on the location of exploration facilities, environmental characteristics, brine exploitation parameters and formal and legal information. These data come from a review of various archives, databases and survey results. Initially, information on the location of the sources should be obtained, which may be available in publicly accessible databases. Next, geological and hydrogeological parameters, which can be obtained from scientific papers and reports, are useful. An important part of the project is also the analysis of legal regulations concerning water extraction and environmental protection. Therefore, data should be obtained from various sources, such as public administration, state institutions or research units. These will serve to develop the database needed to perform further analyses within the BrineRIS research project. It is therefore crucial to carefully collect, analyse and assess the usefulness of the data. [ABSTRACT FROM AUTHOR]

Many societal challenges are threshold dilemmas requiring people to cooperate to reach a threshold before group benefits can be reaped. Yet receiving feedback about others' outcomes relative to one's own (relative feedback) can undermine cooperation by focusing group members' attention on outperforming each other. We investigated the impact of relative feedback compared to individual feedback (only seeing one's own outcome) on cooperation in children from Germany and India (6- to 10-year-olds, N = 240). Using a threshold public-goods game with real water as a resource, we show that, although feedback had an effect, most groups sustained cooperation at high levels in both feedback conditions until the end of the game. Analyses of children's communication (14,374 codable utterances) revealed more references to social comparisons and more verbal efforts to coordinate in the relative-feedback condition. Thresholds can mitigate the most adverse effects of social comparisons by focusing attention on a common goal. [ABSTRACT FROM AUTHOR]

Laypeople believe that sharing their emotional experiences with others will improve their understanding of those experiences, but no clear empirical evidence supports this belief. To address this gap, we used data from four daily life studies (N = 659; student and community samples) to explore the association between social sharing and subsequent emotion differentiation, which involves labeling emotions with a high degree of complexity. Contrary to our expectations, we found that social sharing of emotional experiences was linked to greater subsequent emotion differentiation on occasions when people ruminated less than usual about these experiences. In contrast, on occasions when people ruminated more than usual about their experiences, social sharing of these experiences was linked to lower emotion differentiation. These effects held when we controlled for levels of negative emotion. Our findings suggest that putting feelings into words through sharing may only enable emotional precision when that sharing occurs without dwelling or perseverating. [ABSTRACT FROM AUTHOR]

Happiness has become one of the most important life goals worldwide. However, does valuing happiness lead to better well-being? This study investigates the effect of valuing happiness on well-being using a population-based longitudinal survey of Dutch adults (N = 8,331) from 2019 to 2023. Random-intercept cross-lagged panel models indicated that those who valued happiness generally exhibited higher well-being as manifested by life satisfaction, more positive affect, and less negative affect. However, increases in valuing happiness did not result in changes in life satisfaction 1 year later and had mixed emotional consequences (i.e., increasing both positive and negative affect). Additional analyses using fixed-effects models indicated that valuing happiness had contemporaneous positive effects on well-being. These findings indicate that endorsing happiness goals may have immediate psychological benefits but may not necessarily translate into long-term positive outcomes. [ABSTRACT FROM AUTHOR]

Past work reveals a tight relationship between spatial attention and storage in visual working memory. But is spatially attending an item tantamount to working memory encoding? Here, we tracked electroencephalography (EEG) signatures of spatial attention and working memory encoding while independently manipulating the number of memory items and the spatial extent of attention in two studies of adults (N = 39; N = 33). Neural measures of spatial attention tracked the position and size of the attended area independent of the number of individuated items encoded into working memory. At the same time, multivariate decoding of the number of items stored in working memory was insensitive to variations in the breadth and position of spatial attention. Finally, representational similarity analyses provided converging evidence for a pure load signal that is insensitive to the spatial extent of the stored items. Thus, although spatial attention is a persistent partner of visual working memory, it is functionally dissociable from the selection and maintenance of individuated representations in working memory. [ABSTRACT FROM AUTHOR]

Category learning is a crucial aspect of cognition that involves organizing entities into equivalence classes. Whereas adults tend to focus on category-relevant features, young children often distribute attention between relevant and irrelevant ones. The reasons for children's distributed attention are not fully understood. In two category-learning experiments with adults and with children aged 4, 5, and 6 (N = 201), we examined potential drivers of distributed attention, including (a) immature filtering of distractors and (b) the general tendency for exploration or broad information sampling. By eliminating distractor competition, we reduced filtering demands. Despite identifying the features critical for accurate categorization, children, regardless of their categorization performance, continued sampling more information than was necessary. These results indicate that the tendency to sample information extensively contributes to distributed attention in young children. We identify candidate drivers of this tendency that need to be examined in future research. [ABSTRACT FROM AUTHOR]

The COVID pandemic has shown that when the research community comes together, we can conquer the most complex biomedical challenges. Collaboration and teamwork among federal agencies, private organizations, and researchers have been crucial in the development of vaccines and therapeutics against COVID. Possibly the first example of such cross‐functional collaboration is the Alzheimer's Disease Neuroimaging Initiative (ADNI), the largest and longest continually monitored Alzheimer's study. ADNI was designed and operated as a public‐private partnership, managed by the Foundation for the National Institutes of Health. This article shows how recent successes in the Alzheimer's field are directly a result of ADNI's open and transparent sharing of knowledge, expertise, and resources, which have allowed researchers to advance their understanding of Alzheimer's and tackle challenges in a relatively short period of time. ADNI's approach to open‐source innovation also served as a model for addressing other complex diseases and led to numerous collaborative research initiatives. Highlights: The Alzheimer's Disease Neuroimaging Initiative (ADNI) was designed, structured, and operated as a public‐private partnership, managed by the Foundation for the National Institutes of Health.The recent successes in the Alzheimer's field are directly a result of ADNI's efforts.Open and transparent sharing of knowledge, expertise, and resources allowed researchers to advance their understanding of Alzheimer's and tackle challenges in a relatively short period of time. [ABSTRACT FROM AUTHOR]

The Alzheimer's Disease Neuroimaging Initiative (ADNI) has revolutionized the landscape of Alzheimer's research through its Informatics Core, which has facilitated unprecedented data standardization and sharing. Over 20 years, ADNI established a robust informatics framework, enabling the validation of biomarkers and supporting global research efforts. The Informatics Core, centered at the Laboratory of Neuro Imaging (LONI), provides a comprehensive data hub that ensures data quality, accessibility, and security, fostering over 5600 publications and significant scientific advancements. By embracing open data sharing principles, ADNI set a gold standard in data transparency, allowing over 26,000 investigators from 169 countries to access and download a wealth of multimodal data. This collaborative approach not only accelerated biomarker discovery and drug development and advanced our understanding of Alzheimer's disease but also has served as a model for other research initiatives, demonstrating the transformative potential of carefully designed informatics models and shared data in driving global scientific progress. Highlights: Accelerating biomarker discovery and drug development for Alzheimer's disease.Alzheimer's Disease Neuroimaging Initiative's (ADNI's) open data sharing drives scientific progress.Data exploration and coupled analytics to data archives. [ABSTRACT FROM AUTHOR]

Freshwater crayfish are amongst the largest macroinvertebrates and play a keystone role in the ecosystems they occupy. Understanding the global distribution of these animals is often hindered due to a paucity of distributional data. Additionally, non-native crayfish introductions are becoming more frequent, which can cause severe environmental and economic impacts. Management decisions related to crayfish and their habitats require accurate, up-to-date distribution data and mapping tools. Such data are currently patchily distributed with limited accessibility and are rarely up-to-date. To address these challenges, we developed a versatile e-portal to host distributional data of freshwater crayfish and their pathogens (using Aphanomyces astaci, the causative agent of the crayfish plague, as the most prominent example). Populated with expert data and operating in near real-time, World of Crayfish™ is a living, publicly available database providing worldwide distributional data sourced by experts in the field. The database offers open access to the data through specialized standard geospatial services (Web Map Service, Web Feature Service) enabling users to view, embed, and download customizable outputs for various applications. The platform is designed to support technical enhancements in the future, with the potential to eventually incorporate various additional features. This tool serves as a step forward towards a modern era of conservation planning and management of freshwater biodiversity. [ABSTRACT FROM AUTHOR]

Introduction. Open Government is a form of public policy based on the pillars of collaboration and citizen participation, transparency and the right of access to public information. With the help of information and communication technologies, governments and administrations carry out open data initiatives, making reusable datasets available to all citizens. The academic community, highly qualified personnel, can become potential reusers of this data, which would lead to its use for scientific research, generating knowledge, and for teaching, improving the training of university students and promoting the reuse of open data in the future. Method. This study was developed using a quantitative research methodology (survey), which was distributed by email in one context block and six technical blocks, with a total of 30 questions. The data collection period was between 15 March and 10 May 2021. Analysis. The data obtained through this quantitative methodology were processed, normalised, and analysed. Results. A total of 783 responses were obtained, from 34 Spanish provinces. The researchers come from 47 Spanish universities and 21 research centres, and 19 research areas of the State Research Agency are represented. In addition, a platform was developed with the data for the purpose of visualising the results of the survey. Conclusions. The sample thus obtained is representative and the conclusions can be extrapolated to the rest of the Spanish university teaching staff. In terms of gender, the study is balanced between men and women (41.76% W vs. 56.58% M). In general, researchers responding to the survey know what open data is (79.31%) but only 50.57% reuse open data. The main conclusion is that open government data prove to be useful sources of information for science, especially in areas such as Social Sciences, Industrial Production, Engineering and Engineering for Society, Information and Communication Technologies, Economics and Environmental Sciences. [ABSTRACT FROM AUTHOR]

The concept of the "x-minute city" emphasises connected, mixed-use, and functionally dense urban areas where residents can access most daily necessities within a short walk or bike ride. By promoting proximity to essential destinations and sustainable transport options, this approach reduces the need for extensive travel and minimises environmental impact. This paper analyses the readiness of cities to function as x-minute cities and identifies necessary interventions. Using a reproducible and scalable methodology based on open data and software, the study assesses the accessibility of key urban amenities within specified timeframes. Cumulative accessibility metrics are calculated for different destination categories, considering both walking and cycling. In the case of Seville, accessibility requirements outlined in policy documents are already met for many essential services, particularly public facilities. The study identifies neighbourhoods that excel in accessibility and others that require improvement in adhering to x-minute city principles. The methodology and findings can inform planning and policy decisions in other cities, guiding efforts to enhance amenity provision, test accessibility scenarios, and target intervention areas. [ABSTRACT FROM AUTHOR]

Beaches play a crucial role in recreation and ecosystem habitats, and are central to Australia's national identity. Precise mapping of beach locations is essential for coastal vulnerability and risk assessments. While point locations of over 11,000 beaches are documented from citizen science mapping projects, the full spatial extent and outlines of many Australian beaches remain unmapped. This study leverages deep learning (DL), specifically convolutional neural networks, for binary image segmentation to map beach outlines along the coast of Southeastern Australia. It focuses on Victoria and New South Wales coasts, each approximately 2000 to 2500 km in length. Our methodology includes training and evaluating the model using state-specific datasets, followed by applying the trained model to predict the beach outlines, size, shape, and morphology in both regions. The results demonstrate the model's ability to generate accurate segmentation and rapid predictions, although it faces challenges such as misclassifying cliffs and sensitivity to fine details. Overall, this research presents a significant advancement in integrating DL with coastal science, providing a scalable solution of citizen science mapping efforts for comprehensive beach mapping to support sustainable coastal management and conservation efforts across Australia. Open access datasets and models are provided to further support beach mapping efforts around Australia. [ABSTRACT FROM AUTHOR]

Using publicly available data from 299 preregistered replications from the social sciences, we found that the language used to describe a study can predict its replicability above and beyond a large set of controls related to the article characteristics, study design and results, author information, and replication effort. To understand why, we analyzed the textual differences between replicable and nonreplicable studies. Our findings suggest that the language in replicable studies is transparent and confident, written in a detailed and complex manner, and generally exhibits markers of truthful communication, possibly demonstrating the researchers' confidence in the study. Nonreplicable studies, however, are vaguely written and have markers of persuasion techniques, such as the use of positivity and clout. Thus, our findings allude to the possibility that authors of nonreplicable studies are more likely to make an effort, through their writing, to persuade readers of their (possibly weaker) results. [ABSTRACT FROM AUTHOR]

Statistical learning is a powerful mechanism that enables the rapid extraction of regularities from sensory inputs. Although numerous studies have established that statistical learning serves a wide range of cognitive functions, it remains unknown whether statistical learning impacts conscious access. To address this question, we applied multiple paradigms in a series of experiments (A= 153 adults): Two reaction-time-based breaking continuous flash suppression (b-CFS) experiments showed that probable objects break through suppression faster than improbable objects. A preregistered accuracy-based b-CFS experiment showed higher localization accuracy for suppressed probable (versus improbable) objects under identical presentation durations, thereby excluding the possibility of processing differences emerging after conscious access (e.g., criterion shifts). Consistent with these findings, a supplemental visual-masking experiment reaffirmed higher localization sensitivity to probable objects over improbable objects. Together, these findings demonstrate that statistical learning alters the competition for scarce conscious resources, thereby potentially contributing to established effects of statistical learning on higher-level cognitive processes that require consciousness. [ABSTRACT FROM AUTHOR]

People share information for many reasons. For example, Berger (2011, N = 40) found that undergraduate participants manipulated to have higher physiological arousal were more likely to share a news article with others via email than people who had low arousal. Berger's research is widely cited as evidence of the causal role of arousal in sharing information and has been used to explain why information that induces high-arousal emotions is shared more than information that induces low-arousal emotions. We conducted two replications (N = 111, N = 160) of Berger's study, using the same arousal manipulation but updating the sharing measure to reflect the rise of information sharing through social media. Both studies failed to find an impact of incidental physiological arousal on undergraduate participants' willingness to share news articles on social media. Our studies cast doubt on the idea that incidental physiological arousal--in the absence of other factors--impacts peoples decisions to share information on social networking sites. [ABSTRACT FROM AUTHOR]

The capacity to leverage information from others' opinions is a hallmark of human cognition. Consequently, past research has investigated how we learn from others' testimony. Yet a distinct form of social information--aggregated opinion--increasingly guides our judgments and decisions. We investigated how people learn from such information by conducting three experiments with participants recruited online within the United States (W = 886) comparing the predictions of three computational models: a Bayesian solution to this problem that can be implemented by a simple strategy for combining proportions with prior beliefs, and two alternatives from epistemology and economics. Across all studies, we found the strongest concordance between participants' judgments and the predictions of the Bayesian model, though some participants' judgments were better captured by alternative strategies. These findings lay the groundwork for future research and show that people draw systematic inferences from aggregated opinion, often in line with a Bayesian solution. [ABSTRACT FROM AUTHOR]

Working memory (WM) is a goal-directed memory system that actively maintains a limited amount of task-relevant information to serve the current goal. By this definition, WM maintenance should be terminated after the goal is accomplished, spontaneously removing no-longer-relevant information from WM. Past studies have failed to provide direct evidence of spontaneous removal of WM content by allowing participants to engage in a strategic reallocation of WM resources to competing information within WM. By contrast, we provide direct neural and behavioral evidence that visual WM content can be largely removed less than 1 s after it becomes obsolete, in the absence of a strategic allocation of resources (total N = 442 adults). These results demonstrate that visual WM is intrinsically a goal-directed system, and spontaneous removal provides a means for capacity-limited WM to keep up with ever-changing demands in a dynamic environment. [ABSTRACT FROM AUTHOR]

Recent evidence has shown that social-media platforms like Twitter (now X) reward politically divisive content, even though most people disapprove of interparty conflict and negativity. We document this discrepancy and provide the first evidence explaining it, using tweets by U.S. Senators and American adults' responses to them. Studies la and lb examined 6,135 such tweets, finding that dismissing tweets received more Likes and Retweets than tweets that engaged constructively with opponents. In contrast, Studies 2a and 2b (A= 856; 1,968 observations) revealed that the broader public, if anything, prefers politicians' engaging tweets. Studies 3 (N = 323; 4,571 observations) and 4 (A' = 261; 2,610 observations) supported two distinct explanations for this disconnect. First, users who frequently react to politicians' tweets are an influential yet unrepresentative minority, rewarding dismissing posts because, unlike most people, they prefer them. Second, the silent majority admit that they too would reward dismissing posts more, despite disapproving of them. These findings help explain why popular online content sometimes distorts true public opinion. [ABSTRACT FROM AUTHOR]

Across four studies (N = 816 U.S. adults), we uncovered a gender stereotype about dual pathways to social hierarchy: Men were associated with power, and women were associated with status. We detected this pattern both explicitly and implicitly in perceptions of individuals drawn from Forbes magazine's powerful people lists in undergraduate and online samples. We examined social-cognitive implications, including prominent people's degree of recognition by individuals and society, and the formation of men's and women's self-concepts. We found that power (status) ratings predicted greater recognition of men (women) and lesser recognition of women (men). In terms of the self-concept, we found that women internalized the stereotype associating women with status more than power implicitly and explicitly. Although men explicitly reported having less status and more power than women, men implicitly associated the self with status as much as power. No gender differences emerged in the desires for power and status. [ABSTRACT FROM AUTHOR]

The uniqueness of the European Parliament, as well as the magnitude of impact its decisions wield over member states, are elements that capture researchers' attention. However, several of this institution's particularities have made broad analysis of the textual content it produces difficult. This research note presents Vitrine Démocratique, a new, publicly accessible, and centralized database structuring interventions made in the European Parliament starting in 2014, both in their original languages and translated to English. The process by which this high-velocity database was created is presented, as well as a descriptive overview of the contents of this data source, which is continuously updated on a daily basis. [ABSTRACT FROM AUTHOR]

This article critically examines the domination exerted by big digital companies on the current social, economic, and political context of modern societies, with a particular focus on the implications for the proper functioning of democracy. The objective of this article is to introduce and develop the concept of datafeudalism, expose its emergence for the proper functioning of modern societies and democracy, and to propose courses of action to reverse this situation. To achieve this purpose, firstly, the evolution from surveillance capitalism to datafeudalism will be discussed. Secondly, the structures and operating logic of data feudalism will be analyzed. Thirdly, the harmful impacts of datafeudalism on the proper functioning of the democratic systems of the European Union will be examined. Finally, an attempt will be made to outline courses of action that will make it possible to reverse the situation of economic, social and political tyranny exercised by big digital companies through datafeudalism. [ABSTRACT FROM AUTHOR]

Purpose: T1 mapping is a widely used quantitative MRI technique, but its tissue‐specific values remain inconsistent across protocols, sites, and vendors. The ISMRM Reproducible Research and Quantitative MR study groups jointly launched a challenge to assess the reproducibility of a well‐established inversion‐recovery T1 mapping technique, using acquisition details from a seminal T1 mapping paper on a standardized phantom and in human brains. Methods: The challenge used the acquisition protocol from Barral et al. (2010). Researchers collected T1 mapping data on the ISMRM/NIST phantom and/or in human brains. Data submission, pipeline development, and analysis were conducted using open‐source platforms. Intersubmission and intrasubmission comparisons were performed. Results: Eighteen submissions (39 phantom and 56 human datasets) on scanners by three MRI vendors were collected at 3 T (except one, at 0.35 T). The mean coefficient of variation was 6.1% for intersubmission phantom measurements, and 2.9% for intrasubmission measurements. For humans, the intersubmission/intrasubmission coefficient of variation was 5.9/3.2% in the genu and 16/6.9% in the cortex. An interactive dashboard for data visualization was also developed: https://rrsg2020.dashboards.neurolibre.org. Conclusion: The T1 intersubmission variability was twice as high as the intrasubmission variability in both phantoms and human brains, indicating that the acquisition details in the original paper were insufficient to reproduce a quantitative MRI protocol. This study reports the inherent uncertainty in T1 measures across independent research groups, bringing us one step closer to a practical clinical baseline of T1 variations in vivo. [ABSTRACT FROM AUTHOR]

IMPACT: To foster data-driven policy-making practices there is a need for capacity-building within the public administration (PA) to collect, access, manage and reuse data. This article draws from focus groups and interviews to propose measures to make dataspaces scalable and accessible for smaller actors so that they can participate in data-driven policy-making—both in terms of their capacity to input data in a dataspace and to reuse existing data. This article speaks to PA and data governance scholars, as well as PA and data management practitioners—particularly at the local level in Europe. This article assesses the obstacles and best practices in data gathering and reuse for public administrations (PAs). Through a qualitative analysis, it finds that smaller PAs often lack the resources to harness data in policy-making. This means that data-driven policy-making is currently more applicable to larger organizations. Theoretically, this article critically challenges the idea that pooling data from various sources expands the boundaries of rationality through collective rationality, as several institutional actors remain excluded from data-driven policy-making due to lack of resources. [ABSTRACT FROM AUTHOR]

Smart cities and information and communications technology is a rapidly growing field in both research and real-world implementation, but it is one that is still new and with many different ideas. Unfortunately, there is less cooperation and knowledge sharing across the field, and research often fails to move into real-world applications, which holds it back from becoming fully realized. This paper aims to provide an overview of the current state of smart cities, its definitions, technologies, and technical dimensions, from architectural design to standards and data handling, and how they are handled in the real world and its impact on society. Additionally, it examines important smart city projects, their applications, and ranking systems. This text aims to forecast the future of the field, its impact, the challenges it faces, and what should be addressed to help it reach its full potential. [ABSTRACT FROM AUTHOR]

A log data analysis plays an important role in the uranium mining process. Automating this analysis using machine learning methods improves the results and reduces the influence of the human factor. In particular, the identification of reservoir oxidation zones (ROZs) using machine learning allows a more accurate determination of ore reserves, and correct lithological classification allows the optimization of the mining process. However, training and tuning machine learning models requires labeled datasets, which are hardly available for uranium deposits. In addition, in problems of interpreting logging data using machine learning, data preprocessing is of great importance, in other words, a transformation of the original dataset that allows improving the classification or prediction result. This paper describes a uranium well log (UWL) dataset generated with the employment of floating data windows and designed to solve the problems of identifying ROZ and lithological classification (LC) on sandstone-type uranium deposits. Comparative results of the ways of solving these problems using classical machine learning methods and ensembles of machine learning algorithms are presented. It has been shown that an increase in the size of the floating data window can improve the quality of ROZ classification by 7–9% and LC by 6–12%. As a result, the best-quality indicators for solving these problems were obtained, f1_score_macro = 0.744 (ROZ) and accuracy = 0.694 (LC), using the light gradient boosting machine and extreme gradient boosting, respectively. [ABSTRACT FROM AUTHOR]

By implementing a novel synthetic difference-in-differences approach, this article assesses whether adopting open data policy will increase the impact of a journal. Using China Industrial Economics (hereafter CIE, a leading Chinese journal which mandates authors to open their research data/code in the end of 2016) as a quasi-experiment, we show that the introduction of open data policy leads to an increase in CIE's impact factor by a factor of approximately 1.5 to 2 times. [ABSTRACT FROM AUTHOR]

Abstract The growing demand for alternative energy sources to alleviate environmental impacts highlights the need to move from fossil fuels to renewable energy. This study demonstrated the technical feasibility of using a solar photovoltaic (PV) system for the production of green hydrogen. This research examined electrical and power data from a PV plant in Irecê, Bahia, using open data sources to provide insights into the production of green hydrogen from renewable sources. The system mainly depends on the use of a renewable source, PV solar energy, integrated with batteries, electrolyzers, and hydrogen tanks. Electrolyzer, battery, and hydrogen tank sizing analysis for optimal hydrogen production was effectively conducted using HOMER Energy software. The predicted system topology prioritizes a local DC network, optimizing efficiency for electrolyzers that have inherently low efficiency. The electrolyzer simulation involves initial Python‐based sizing and comprehensive sizing with HOMER Energy software, ensuring accuracy within a 10% discrepancy limit. This highlights the importance of analytical calculations and optimization software for sizing more complex systems.

Abstract Open research data refer to publicly available scientific information that can be accessed free of charge, usually provided by public data sources. Users must comply with specific requirements set by the institutions providing the data and always acknowledge the source of the data when processing, transmitting, storing or publishing it. One of the tasks of the BrineRIS project is the mapping of brine resources, requiring reliable data on the location of exploration facilities, environmental characteristics, brine exploitation parameters and formal and legal information. These data come from a review of various archives, databases and survey results. Initially, information on the location of the sources should be obtained, which may be available in publicly accessible databases. Next, geological and hydrogeological parameters, which can be obtained from scientific papers and reports, are useful. An important part of the project is also the analysis of legal regulations concerning water extraction and environmental protection. Therefore, data should be obtained from various sources, such as public administration, state institutions or research units. These will serve to develop the database needed to perform further analyses within the BrineRIS research project. It is therefore crucial to carefully collect, analyse and assess the usefulness of the data.

The Oum Er-Rbia watershed faces a critical challenge in effectively disseminating the results of groundwater and surface water quality assessments to stakeholders and the public. This research aimed to address this challenge by developing a method for synthesizing and visualizing knowledge through web mapping. The methodology followed involved collecting data on surface and groundwater quality, hydrogeology, human impact, and topography. Subsequently, a needs analysis was conducted to identify the requirements of potential users (land managers, water users, researchers) to define system functionalities. Spatial data was organized into thematic layers (e.g., hydrography, geology) with detailed attributes to address user queries and generate maps. Open-source technologies were employed to build a web-based system (SCIEM) allowing users to visualize, analyze, and explore water quality data. Finally, user evaluation confirmed SCIEM's effectiveness in disseminating and analyzing water quality information within the watershed. The developed web mapping system has successfully addressed the challenge of disseminating research results by establishing an online, interactive multi-scale mapping system for groundwater and surface water characterization. This system facilitates the presentation of maps with a simple click, making them accessible to both geospatial experts and non-specialists. As a result, stakeholders such as water managers, land-use planners, and even farmers and citizens can now access a comprehensive view of groundwater and surface water information. Additionally, the utilization of free and open-source software (IMSMS) has proven to be a cost-effective and efficient approach. The Interactive Multi-Scale Mapping System (IMSMS) provides a valuable tool for disseminating water quality data within the Oum Er-Rbia watershed. Building on this success, future research can explore the potential of adapting the IMSMS framework to other regions facing water quality challenges. Additionally, incorporating new data types beyond water management could provide a more holistic understanding of the interconnected factors influencing water resources.

Processing social information from faces is difficult for individuals with autism spectrum disorder (ASD). However, it remains unclear whether individuals with ASD make high-level social trait judgments from faces in the same way as neurotypical individuals. Here, we comprehensively addressed this question using naturalistic face images and representatively sampled traits. Despite similar underlying dimensional structures across traits, online adult participants with self-reported ASD showed different judgments and reduced specificity within each trait compared with neurotypical individuals. Deep neural networks revealed that these group differences were driven by specific types of faces and differential utilization of features within a face. Our results were replicated in well-characterized in-lab participants and partially generalized to more controlled face images (a preregistered study). By investigating social trait judgments in a broader population, including individuals with neurodevelopmental variations, we found important theoretical implications for the fundamental dimensions, variations, and potential behavioral consequences of social cognition.

The purpose of the current study was to test the longitudinal association between disordered eating symptoms (body dissatisfaction, drive for thinness and bulimia) in adolescence (ages 12, 14, 16, 18, 19) and adulthood (age 40) in a sample of 883 white and Black women. We also investigated moderation by race. Adolescent symptoms at each time point significantly predicted adulthood symptoms for the body dissatisfaction and drive for thinness subscales, for both Black and white women. Bulimia symptoms in adolescence predicted symptoms in adulthood; however, the effect was largely driven by white women. Although moderation was non-significant, among white women, bulimia symptoms at all adolescent time points predicted adulthood bulimia, but among Black women, only symptoms at ages 18 and 19 were predictive of adulthood bulimia. Results suggest that both Black and white women are susceptible to disordered eating and that symptoms emerging in adolescence can potentially follow women into midlife.

While the phrase ‘public service’ commonly refers to public bodies based on legal principles, it can also be understood as the services rendered to the public. Therefore, fostering the relationship of trust between services and citizens – as the users of these public services – involves thinking very finely about attracting audiences and the interactions between users and politicians through public communication, regardless of the channel used. The low use of open data and information in public action translates into a poor level of trust towards public institutions in Romania, with no clear vision to urgently improve the situation. In order to change the architecture of the data of public interest, the central institutions must act towards the citizen’ environment choice and therefore, will improve the public trust in institutions.

The purpose of this study was to identify the specific features of young people’s political activity in the context of digital transformation and to assess the potential of digital tools to overcome the crisis of trust in democratic insti- tutions. The study employed a case study method to analyse digital “revolutions”, a comparative analysis of the levels of political activity of young people in different countries, and a content analysis of social media to investigate new forms of political participation. The findings of the study revealed substantial differences in young people’s political engagement between developed and developing democracies. Developed countries show a declining trend in conventional forms of po- litical participation, while the role of digital platforms in political mobilisation is growing. In developing countries, digital technologies are often used as a tool for protest and struggle against authoritarian regimes. The study revealed a tendency towards individualisation of political participation and globalisation of the political consciousness of young people, which is manifested in a focus on concrete issues and participation in transnational movements. The analysis showed that digital platforms are creating new opportunities for political participation, including through online petitions, crowdfunding of political campaigns, and digital platforms for participatory democracy. At the same time, the risks associated with digital inequality, manipulation, and disinformation in the online space were identified. The study demonstrated the potential of digital tools to increase transparency and accountability of the authorities, which can help restore young people’s trust in democratic institutions. Specifically, the study covered the effectiveness of such tools as open data portals, e-government platforms, and e-voting systems. The analysis of new vectors in the development of digital democracy, such as the integra- tion of artificial intelligence and blockchain technologies, has revealed considerable potential for transforming political processes, but also highlighted the need to accommodate the ethical aspects and potential risks of their implementation. The findings of this study are significant for developing strategies to engage young people in political processes and mod- ernise democratic institutions in the digital age

Abstract Background Governments worldwide are facing growing pressure to increase transparency, as citizens demand greater insight into decision-making processes and public spending. An example is the release of open healthcare data to researchers, as healthcare is one of the top economic sectors. Significant information systems development and computational experimentation are required to extract meaning and value from these datasets. We use a large open health dataset provided by the New York State Statewide Planning and Research Cooperative System (SPARCS) containing 2.3 million de-identified patient records. One of the fields in these records is a patient’s length of stay (LoS) in a hospital, which is crucial in estimating healthcare costs and planning hospital capacity for future needs. Hence it would be very beneficial for hospitals to be able to predict the LoS early. The area of machine learning offers a potential solution, which is the focus of the current paper. Methods We investigated multiple machine learning techniques including feature engineering, regression, and classification trees to predict the length of stay (LoS) of all the hospital procedures currently available in the dataset. Whereas many researchers focus on LoS prediction for a specific disease, a unique feature of our model is its ability to simultaneously handle 285 diagnosis codes from the Clinical Classification System (CCS). We focused on the interpretability and explainability of input features and the resulting models. We developed separate models for newborns and non-newborns. Results The study yields promising results, demonstrating the effectiveness of machine learning in predicting LoS. The best R2 scores achieved are noteworthy: 0.82 for newborns using linear regression and 0.43 for non-newborns using catboost regression. Focusing on cardiovascular disease refines the predictive capability, achieving an improved R2 score of 0.62. The models not only demonstrate high performance but also provide understandable insights. For instance, birth-weight is employed for predicting LoS in newborns, while diagnostic-related group classification proves valuable for non-newborns. Conclusion Our study showcases the practical utility of machine learning models in predicting LoS during patient admittance. The emphasis on interpretability ensures that the models can be easily comprehended and replicated by other researchers. Healthcare stakeholders, including providers, administrators, and patients, stand to benefit significantly. The findings offer valuable insights for cost estimation and capacity planning, contributing to the overall enhancement of healthcare management and delivery.

The openness of government data is considered essential and is an influential innovation in society to prevent corruption by increasing the transparency of information and data. One of the drivers for the emergence of Open Government Data (OGD) is the Industrial Revolution 4.0 era, which has influenced all aspects of life with all technological advances. Society needs open information to support and monitor the running of Government. However, it still needs to be determined whether the published OGD data is actual data or only data presented to cancel obligations to protect certain powers. From here, the author wants to conduct a study on OGD parameters using the Eisenhower matrix after reviewing several experts in the telecommunications sector as a basis for identifying parameters for the openness of information and government data in Indonesia. From the results of the analysis that we have carried out, it is found that government support is the highest parameter, followed by other parameters, namely policy, data transparency in the OGD portal, and the participation of citizens as the most crucial support in supporting the implementation of quality and quality OGD data publication in Indonesia. So, the final aim of this study is to provide a future view for all policies that will be made by the Government related to OGD to include all parameters that support data transparency that can be used optimally by the general public so that actual data publication is achieved that does not protect authority or specific power.

The 25th annual Bioinformatics Open Source Conference (BOSC 2024, open-bio.org/events/bosc-2024) was part of the 2024 conference on Intelligent Systems for Molecular Biology (ISMB 2024). Launched in 2000 and held yearly since, BOSC is the premier meeting covering open-source bioinformatics and open science. ISMB 2024 was held in Montréal, Canada, with an online participation option. A total of nearly 2000 people attended; about 200 people participated in BOSC sessions. Over the course of two days, BOSC covered a wide range of topics in open science and open source bioinformatics, including Data Analysis, Open Data, Visualization, Developer Tools and Libraries, Standards and Frameworks for Open Science, and Open AI/ML. Mélanie Courtot delivered an impactful first keynote with a perspective on how “The Data Shows We Need Better Data”. The second keynote speaker, Andrew Su, discussed “Open Data, Knowledge Graphs, and Large Language Models.” BOSC ended with a panel, “Open Source AI/ML: A Game Changer for Bioinformatics?,” in which Lawrence Hunter and Thomas Hervé Mboa Nkoudou joined BOSC’s keynote speakers as panelists. Immediately following BOSC, the CollaborationFest was held at Montréal’s University of Québec campus. First launched in 2010, CoFest is a collaborative work event held yearly around BOSC. This year’s CoFest included 42 participants who worked together on 10 projects.